(1) Field of the Invention
The present invention relates to a neurosystem and to a method for signal classification whose structures are a-priori known and which is especially useful in fields of utility in which economy of computational burden, accuracy of classification, or ease of accommodating additional signals to be classified are critical factors in choice of design.
(2) Description of the Prior Art
Signal classification involves the extraction and partition of features of targets of interest. In many situations, the problem is complicated by the uncertainty of the signal origin, fluctuations in the presence of noise, the degree of data correlation in multi-sensor systems, and the interference of nonlinearities in the environment. Research and studies in the past have focused on developing robust and efficient methods and devices for recognizing patterns in signals, many of which have been developed from traditional signal processing techniques, and known artificial neural network technology.
FIG. 1 is a schematic representation of a conventional pattern recognition system. In this configuration, the system consists of three phases: data acquisition 10, data preprocessing 12, and decision classification 14. In the data acquisition phase 10, analog data from the physical world are gathered through a transducer and converted to digital format suitable for computer processing. In this stage, the physical variables are converted into a set of measured data, indicated in FIG. 1 by electric signals, x(r), if the physical variables are sound (or light intensity) and the transducer is a microphone (or photocells). The measured data is used as inputs to the second phase 12 (data preprocessing) and is grouped into a set of characteristic features, P(i), as output to third phase 14. The third phase 14 is actually a classifier or pattern recognizer which is in the form of a set of decision functions. Based on the distinction of feature characteristics in P(i), the classifier in this phase will determine the category of the underlying signals.
Signal classification or pattern recognition methods are often classified as either parametric or nonparametric. For some classification tasks, pattern categories are known a priori to be characterized by a set of parameters. A parametric approach is to define the discriminant function by a class of probability densities with a relatively small number of parameters. Since there exist many other classification problems in which no assumptions can be made about these parameters, nonparametric approaches are designed for those tasks. Although some parameterized discriminant functions, e.g., the coefficients of a multivariate polynomial of some degree are used in nonparametric methods, no conventional form of the distribution is assumed.
In recent years, one of the nonparametric approaches for pattern classification is neural network training. In neural network training for pattern classification, there are a fixed number of categories (classes) into which stimuli (activation) are to be classified. To resolve it, the neural network first undergoes a training session, during which the network is repeatedly presented a set of input patterns along with the category to which each particular pattern belongs. Then later on, a new pattern is presented to the network which has not been seen before but which belongs to the same population of patterns used to train the network. The task for the neural network is to classify this new pattern correctly. Pattern classification as described here is a supervised learning problem. The advantage of using a neural network to perform pattern classification is that it can construct nonlinear decision boundaries between the different classes in nonparametric fashion, and thereby offers a practical method for solving highly complex pattern classification problems.
The discrete Fourier transform (DFT) has had a great impact on many applications of digital signal processing. Not only does the DFT provide data decorrelation, but it also greatly reduces the computational requirements. A standard approach for analyzing a signal is to decompose it into a sum of simple building blocks. The fast Fourier transform (FFT) and discrete cosine transform (DCT) are the most well-known examples. However, once the basis vector formed by the Fourier kernel function is a cosine basis, it does not have compact support or finite energy. Thus, a large number of transform coefficients are required to retain a significant fraction of the total signal energy.
In the past several decades, signal characterizations have been mainly performed with traditional spectral processing such as the DFT and FFT. Signal characteristics are represented by frequency information. Based on its frequency function, or spectral information, the signal is modeled for analyzing and processing. However, Fourier transform outputs do not contain information in the time domain. Critical details of the signal as it evolves over time are lost. Therefore, difficulty arises in processing the data, especially if the data is nonstationary or nonlinear. Recently, wavelets and wavelet transforms have emerged as a useful alternative for many applications in signal processing. Since their basis functions have compact support and their transforms have good localization in both time and frequency domains, wavelets have opened up new avenues for improving signal processing. By a wavelet transform of a given function g(t), one can represent the function as follows: ##EQU1## where n and k are integer indexes and the .upsilon..sub.nk are the coefficients. Each of the functions .upsilon..sub.nk (t) belongs to one of a finite number of families {.upsilon..sub.nk (t)}, and the parameters n and k are related to the frequency scale and time location of this function.
Despite these advances, there still remains a need however for systems and methods of pattern classification which perform at a high level.